Details
| Original language | English |
|---|---|
| Title of host publication | 2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025 |
| Publisher | IEEE Computer Society |
| Pages | 153-158 |
| Number of pages | 6 |
| ISBN (electronic) | 9798331522469 |
| ISBN (print) | 979-8-3315-2247-6 |
| Publication status | Published - 17 Aug 2025 |
| Event | 21st IEEE International Conference on Automation Science and Engineering, CASE 2025 - Los Angeles, United States Duration: 17 Aug 2025 → 21 Aug 2025 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| ISSN (Print) | 2161-8070 |
| ISSN (electronic) | 2161-8089 |
Abstract
Flexible part feeding is a key challenge in modern automated production, where increasing uncertainties, shorter product life cycles, and cost pressures require adaptable solutions. Aerodynamic part feeding systems, which use controlled air jets to manipulate workpieces, offer a retooling-free alternative to traditional vibratory bowl feeders. To ensure precise workpiece handling, reliable pose classification is essential. This paper presents a machine learning-based framework for classifying workpiece poses using a class of convolutional neural networks (CNNs) called YOLO and an industrial camera. Instead of relying on manually labeled real-world images-which would introduce machine downtimes and increased setup efforts-the proposed method trains CNNs exclusively on synthetic datasets. Artificial images of workpieces in various poses are generated from CAD models using the open-source rendering engine Blender. Multiple CNN architectures are trained and evaluated, achieving a classification precision exceeding 95 % for most workpieces when tested on real workpiece images. The results demonstrate that the approach enables accurate and efficient workpiece pose classification without the need for labor-intensive dataset creation. While developed for aerodynamic part feeding, the proposed method is applicable to a wide range of industrial scenarios requiring automated workpiece orientation classification.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
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2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025. IEEE Computer Society, 2025. p. 153-158 (IEEE International Conference on Automation Science and Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - You Only Look Once, But the Parts Keep Moving
T2 - 21st IEEE International Conference on Automation Science and Engineering, CASE 2025
AU - Shieff, Dasha
AU - Akchi, Mohamed
AU - Raatz, Annika
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/8/17
Y1 - 2025/8/17
N2 - Flexible part feeding is a key challenge in modern automated production, where increasing uncertainties, shorter product life cycles, and cost pressures require adaptable solutions. Aerodynamic part feeding systems, which use controlled air jets to manipulate workpieces, offer a retooling-free alternative to traditional vibratory bowl feeders. To ensure precise workpiece handling, reliable pose classification is essential. This paper presents a machine learning-based framework for classifying workpiece poses using a class of convolutional neural networks (CNNs) called YOLO and an industrial camera. Instead of relying on manually labeled real-world images-which would introduce machine downtimes and increased setup efforts-the proposed method trains CNNs exclusively on synthetic datasets. Artificial images of workpieces in various poses are generated from CAD models using the open-source rendering engine Blender. Multiple CNN architectures are trained and evaluated, achieving a classification precision exceeding 95 % for most workpieces when tested on real workpiece images. The results demonstrate that the approach enables accurate and efficient workpiece pose classification without the need for labor-intensive dataset creation. While developed for aerodynamic part feeding, the proposed method is applicable to a wide range of industrial scenarios requiring automated workpiece orientation classification.
AB - Flexible part feeding is a key challenge in modern automated production, where increasing uncertainties, shorter product life cycles, and cost pressures require adaptable solutions. Aerodynamic part feeding systems, which use controlled air jets to manipulate workpieces, offer a retooling-free alternative to traditional vibratory bowl feeders. To ensure precise workpiece handling, reliable pose classification is essential. This paper presents a machine learning-based framework for classifying workpiece poses using a class of convolutional neural networks (CNNs) called YOLO and an industrial camera. Instead of relying on manually labeled real-world images-which would introduce machine downtimes and increased setup efforts-the proposed method trains CNNs exclusively on synthetic datasets. Artificial images of workpieces in various poses are generated from CAD models using the open-source rendering engine Blender. Multiple CNN architectures are trained and evaluated, achieving a classification precision exceeding 95 % for most workpieces when tested on real workpiece images. The results demonstrate that the approach enables accurate and efficient workpiece pose classification without the need for labor-intensive dataset creation. While developed for aerodynamic part feeding, the proposed method is applicable to a wide range of industrial scenarios requiring automated workpiece orientation classification.
UR - http://www.scopus.com/inward/record.url?scp=105018334427&partnerID=8YFLogxK
U2 - 10.1109/CASE58245.2025.11163971
DO - 10.1109/CASE58245.2025.11163971
M3 - Conference contribution
AN - SCOPUS:105018334427
SN - 979-8-3315-2247-6
T3 - IEEE International Conference on Automation Science and Engineering
SP - 153
EP - 158
BT - 2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025
PB - IEEE Computer Society
Y2 - 17 August 2025 through 21 August 2025
ER -